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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

Data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springe

关键词： Internet of things

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Online Linear Regression in Dynamic Environments via Discounting

arXiv

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arXiv 2024年

作者： Jacobsen, Andrew Cutkosky, Ashok Department of Computing Science University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Boston University BostonMA United States

We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees even in the complete absence of prior knowledge. We present a novel analysis showing that a discounted variant of the Vovk-Azoury-Warmuth forecaster achieves dynamic regret of the form RT (u) ≤ O (d log(T) ∨ √dPTγ (u)T), where PTγ (u) is a measure of variability of the comparator sequence, and show that the discount factor achieving this result can be learned on-the-fly. We show that this result is optimal by providing a matching lower bound. We also extend our results to strongly-adaptive guarantees which hold over every sub-interval [a, b] ⊆ [1, T] simultaneously. © 2024, CC BY.

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Guest Editorial:Special issue on recent developments in advanced mechatronics systems

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 547-548页

作者： Lin Meng Mingcong Deng Hongnian Yu Shengjun Wen Department of Electronic and Computer Engineering Ritsumeikan UniversityKusatsuJapan Department of Electrical and Electronic Engineering Tokyo University of Agriculture and TechnologyFuchuJapan School of Engineering and the Built Environment Edinburgh Napier UniversityEdinburghUK Zhongyuan-Petersburg Aviation College Zhongyuan University of TechnologyZhengzhouChina

This Special Issue will accept contributions describing innovative research and developments in‘Advanced Mechatronic Systems’,and will cover a wide range of disciplines,including robotics,automation and control systems,and new energy *** particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications in advanced mechatronic *** quantitative engineering and science studies may be considered as well.

关键词： mec innovative automation

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Retrieval-augmented Generation for GenAI-enabled Semantic Communications

arXiv

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arXiv 2024年

作者： Tang, Shunpu Zhang, Ruichen Yan, Yuxuan Yang, Qianqian Niyato, Dusit Wang, Xianbin Mao, Shiwen College of Information Science and Electronic Engineering Zhejiang University Hangzhou China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applications. However, current GenSemCom systems still face challenges such as semantic inconsistency, limited adaptability to diverse tasks and dynamic environments, and the inability to leverage insights from past transmission. Motivated by the success of retrieval-augmented generation (RAG) in the domain of GenAI, this paper explores the integration of RAG in GenSemCom systems. Specifically, we first provide a comprehensive review of existing GenSemCom systems and the fundamentals of RAG techniques. We then discuss how RAG can be integrated into GenSemCom. Following this, we conduct a case study on semantic image transmission using an RAG-enabled diffusion-based SemCom system, demonstrating the effectiveness of the proposed integration. Finally, we outline future directions for advancing RAG-enabled GenSemCom systems. © 2024, CC BY.

关键词： Latent semantic analysis

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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computer Systems Science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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BITLITE: Light Bit-wise Operative Vector Matrix Multiplication for Low-Resolution Platforms

BITLITE: Light Bit-wise Operative Vector Matrix Multiplicati...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Vince Tran Demeng Chen Roman Genov Mostafa Rahimi Azghadi Amirali Amirsoleimani Department of Electrical and Computer Engineering University of Toronto Toronto Canada College of Science and Engineering James Cook University Townsville Australia Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As machine learning (ML) algorithms, particularly neural networks (NN), expand in popularity and capacity, the quest for more efficient computation methods gains momentum. Memristor crossbar technology emerges as a promising alternative to traditional computing units, aiming to address traditional computing challenges. However, conventional matrix-vector multiplication (MVM) methods on these platforms are often plagued by device imperfections and drift. In this work, we introduce an innovative lightweight calculation approach leveraging bit-transformation for MVM, significantly enhancing operation precision and, consequently, the performance of ML algorithms on memristor crossbar platforms. We provide details of the core algorithm and its extensions, furnish digital validation, and simulate its efficacy using an autoencoder (AE) neural network with an extended VTEAM model. Our tests demonstrate an average reconstruction precision improvement of approximately 53.5%. This work’s applicability extends beyond NNs, offering a foundational method for conducting more precise analog MVM operations.

关键词： Performance evaluation Machine learning algorithms Circuits and systems Memristors Artificial neural networks Machine learning Approximation algorithms

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Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology 11

Enhancing Facility Safety for Autonomous Gas Inspection Dron...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Latha, S. Asha, P. Srinivasan, V. Prasanna Elangovan, K. Thamizhamuthu, R. Sujatha, S. Department of Biotechnology Tamil Nadu Salem India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To increase facility safety by integrating autonomous gas inspection drones with Convolutional Neural Networks (CNNs) and Internet of Things (loT) technologies. Due to their ability to reach difficult situations, unmanned aerial vehicles are increasingly used for gas inspection in industry. However, drone safety and gas detection accuracy remain major issues. It offers a comprehensive method that uses CNN s for real-time image analysis and classification to help the drone locate gas leaks with high accuracy. CNN is trained on a broad dataset of gas leak situations and environmental conditions. loT technology also allows the drone and centralized monitoring system to communicate in real-time for data sharing and decision-making. Our method can adjust to changing lighting and weather and discriminate between innocuous abnormalities and gas leaks. Advanced navigation algorithms let the drone traverse complicated industrial sites while avoiding obstacles and maximizing coverage. Experimental findings show the suggested technology improves facility safety by lowering gas leak reaction times and operator risk. CNNs with loT technologies increase gas leak detection and allow proactive maintenance, improving industrial facility safety and efficiency. It advances autonomous systems for industrial applications by tackling gas inspection safety problems. The suggested framework lays the groundwork for integrating innovative technology to improve autonomous gas inspection drone capabilities and safety. © 2024 IEEE.

关键词： Gases

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SpikeMotion: A Transformer Framework for High - Throughput Video Segmentation on FPGA

SpikeMotion: A Transformer Framework for High - Throughput V...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Uchechukwu Leo Udeji Martin Margala Electrical and Computer Engineering department University of Massachusetts Lowell Lowell USA Computing and Informatics department University of Louisiana at Lafayette Lafayette USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Transformers have emerged at the forefront in the training and inference of diverse machine learning tasks, encompassing video processing, image generation and classification, and natural language processing (NLP). Despite their increasing prevalence, a comprehensive framework to efficiently implement them has been lacking. This study introduces a transformer-based framework which accelerates image processing of UNETR (U-shaped neural network transformer) model for video segmentation task using the cityscapes dataset. Given the large size of images in the dataset we incorporate hyperattention and mixed precision in our design. Our model is trained on Google A1OO GPU accelerator and profiled. Finally, our design is implemented on FPGA to take advantage of the reconfigurable and high-throughput characteristics of system-on-chips (SoC) for image processing. Our results indicate improvements compared to existing research in this domain.

关键词： Training Image segmentation Neural networks Memory management Graphics processing units Machine learning Transformers

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Reactive Navigation of the Ackermann Steering Robot in Unknown Environments

Reactive Navigation of the Ackermann Steering Robot in Unkno...

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International Conference on Autonomous Robots and Agents, ICARA

作者： ANA ŠELEK IVAN HRABAR SAŠA ŠTEKOVIĆ Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Autegra D.O.O. Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Mobile robots are now widely used in numerous real-world applications that have complex navigation requirements, especially in environments used by humans. This requires highly accurate navigation that can be performed in realtime. In this paper, a method for generating a smooth motion of nonholonomic mobile robots is proposed. It enables robots to move optimally toward the desired goal and allows fast path replanning when encountering unknown or dynamic obstacles. The method generates smooth, collision-free trajectories based on clothoids, ensuring high computational efficiency and suitability for realtime path planning. By applying a smoothing algorithm, the proposed method improves the robot's efficiency in terms of travel time and trajectory length from start to goal, as demonstrated by a comparison with model predictive control.

关键词： Smoothing methods Navigation Dynamics Real-time systems Trajectory Computational efficiency Mobile robots Collision avoidance Testing Predictive control

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SAR-MemPipe: A Hybrid Pipeline-SAR Memristive ADC for Analog Resistive Arrays

SAR-MemPipe: A Hybrid Pipeline-SAR Memristive ADC for Analog...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hao You Jianxiong Xu Amirali Amirsoleimani Mostafa Rahimi Azghadi Roman Genov Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Engineering and Computer Science York University Toronto Canada College of Science and Engineering James Cook University Townsville Australia

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper presents a hybrid pipeline SAR ADC with a loop-unrolled structure to reduce the crossbar’s ADC power while maintaining high speed. The 1 st stage memristive SAR ADC can fully utilize the TIA originally in the crossbar and avoid the extra MDAC in pipeline ADC. Further, memristive weight calibration and a new resistive alternated binary search are implemented on the 1 st stage to maintain the TIA gain and ADC’s accuracy. Both stage’s ADC are loop-unroll to eliminate the delay brought by SAR logic for high speed. Through multiple simulations, the design is demonstrated to be robust to the frequency and mismatch variations with the highest sampling frequency reaching 300MHz and SNDR up to 65.1dB in 9MHz input. The power consumption is designed to be as low as 6.7mW, which helps the ADC to achieve a 15.2fJ/conv FoM. Not limited to the crossbar, the presented ADC also shows promising potential for applications in various fields (biomedical, IoT etc.) for general purposes.

关键词： Time-frequency analysis Accuracy Power demand Pipelines computer architecture Robustness Delays

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